Hepatitis E virus (HEV) is a major cause of acute viral hepatitis, with an estimated 20 million human infections annually. Its diverse host range presents a significant zoonotic threat, yet the molecular mechanisms underpinning HEV-host interactions remain poorly characterised. Here, we used transposon-based insertion screening of the HEV pORF1 polyprotein to identify a novel region within the viral PCP domain that permits HA-epitope insertion, enabling the first epitope tagging of this domain. Combined with V5 tagging of the hypervariable region (HVR), this approach enabled the generation of a replication-competent stable cell line expressing dual-tagged ORF1. Co-immunoprecipitation and TMT-based LC-MS proteomics revealed that pORF1 interacts with host proteins involved in endoplasmic reticulum-associated degradation (ERAD) and the 26S proteasome. Pharmacological inhibition demonstrated that both pathways are required for efficient HEV replication. Treatment with the FDA-approved proteasome inhibitor bortezomib altered pORF1 precursor abundance and led to loss of full-length pORF1 through enhanced proteolysis. Importantly, the kinetics of ORF1 degradation following proteasome inhibition correlated with immediate loss of HEV replication, demonstrating a functional link between pORF1 proteostasis and viral replication. Collectively, our findings identify the ERAD network as a previously unrecognised requirement for HEV replication and establish a role for the 26S proteasome in maintaining pORF1 proteostasis.

Accurate chromosome segregation requires precise spindle organization, but how spindle architecture adapts to genome size remains unclear. A study now highlights a general scaling law based on chromos...

Mutational scars act as biomarkers for inaccurate DNA repair. Here, the authors systematically interrogate the contribution of every protein-coding gene to DNA double-strand break repair by generating outcome profiles following gene knockouts to extract mechanistic insight from profile alterations.

To identify drug targets and medicines that could be repurposed for AD, we developed a genome–phenome association analytic framework composed of three key components: (1) genetics-based MR analysis (Fig. 1a); (2) experimental validation in neurons and brain organoids derived from patient-derived induced pluripotent stem (iPS) cells, as well as in a transgenic mouse model of AD (5xFAD) (Fig. 1b); and (3) real-world patient data-based drug repurposing validation (Fig. 1c). We conducted MR analyses to identify potential drug targets from 1,229 druggable proteins (Extended Data Fig. 1). A protein was considered druggable if targeted by known FDA-approved medicines or investigational molecules with binding affinity lower than 1 µM (Methods). Next, we selected single nucleotide polymorphisms (SNPs) as valid instrumental variables (IVs; false discovery rate (FDR)-adjusted P values < 0.05 and F-statistics > 10) for each protein-coding gene from three protein quantitative trait loci (pQTL) and nine gene expression quantitative trait loci (eQTL) datasets (Fig. 1, Extended Data Fig. 1 and Supplementary Table 1) derived from the Religious Orders Study/Memory and Aging Project (ROSMAP)14, MetaBrain15 and Mayo Clinic Biobank16. We also gathered seven AD GWAS summary statistics datasets from EA or AA individuals (Supplementary Table 2) and implemented five complementary MR models17,18...

This revision of the APG classification considers the extensive recent analyses of hundreds of nuclear and plastid genes for many angiosperm species. Although previous versions of the APG classificat...

Read the full paper: CREAT: A CRISPR‐Based Genome Trimming Strategy for Systematic Identification of Dispensable Regions and Rapid Genome Reduction

Read the full paper: Turnip Mosaic Virus‐Based gRNA Delivery System for Plant Genome Editing

Deep-learning-based AI tools are helping researchers probe challenging regions of the genome and accelerating the pace of genetic discoveries, particularly in rare disease research.

The noncoding genome includes key regulatory elements, yet functional dissection remains challenging. Here, authors benchmarked five CRISPR-based perturbation tools and developed a deep learning model to guide pgRNA design, providing a practical framework for decoding noncoding genome function.

Increased understanding of the functional complexity of the genome has led to growing recognition of the role of non-sequence-based regulatory variation in disorders of the human central nervous syste...

Genome-wide data from Bronze Age individuals across nine sites in the Southern Levant show strong genetic resemblance, including a component from populations related to Chalcolithic Zagros and Early B...

Phage Tn-seq uses Tn5 transposon mutagenesis with anti-CRISPR-based selection and deep sequencing as a method to generate unbiased, genome-wide mutations across diverse phages

An updated phylogeny of bats&nbsp;is presented,&nbsp;based on new genome assemblies and&nbsp;many ancient fossils&nbsp;and including all known bat families.

Respiratory pathogens remain a major global public health concern due to factors including their genetic diversity, dynamic transmission patterns, and increasing antimicrobial resistance. Conventional molecular diagnostics, particularly polymerase chain reaction (PCR)-based assays, remain essential for rapid and scalable pathogen detection but provide limited information on pathogen evolution, transmission, and resistance mechanisms. Advances in whole-genome sequencing (WGS), targeted sequencing, and metagenomic next-generation sequencing (mNGS) have expanded respiratory pathogen surveillance from targeted detection toward higher-resolution genomic characterization. However, no single technology can adequately address all surveillance objectives. Instead, molecular diagnostics, genomic sequencing, epidemiological investigation, and clinical assessment provide complementary information that can be integrated according to the pathogen, clinical context, and specific public health question. This review examines the evolution of respiratory pathogen surveillance and emphasizes an integrated framework in which different technologies are selected and combined to support pathogen characterization, transmission investigation, antimicrobial resistance surveillance, and preventive decision-making. We further discuss emerging applications of artificial intelligence (AI) for integrating genomic, epidemiological, and clinical data, with potential roles in genomic interpretation, early risk assessment, and precision infection control. Although AI-enhanced surveillance remains largely at the research and validation stage, advances in data integration, model development, and computational infrastructure may enable more predictive and actionable surveillance systems. Overall, the future of respiratory pathogen surveillance will depend not simply on adopting more advanced technologies, but on integrating complementary detection and analytical approaches to transform pathogen detection into actionable evidence for public health decision-making.

Respiratory pathogens remain a major global public health concern due to factors including their genetic diversity, dynamic transmission patterns, and increasing antimicrobial resistance. Conventional molecular diagnostics, particularly polymerase chain reaction (PCR)-based assays, remain essential for rapid and scalable pathogen detection but provide limited information on pathogen evolution, transmission, and resistance mechanisms. Advances in whole-genome sequencing (WGS), targeted sequencing, and metagenomic next-generation sequencing (mNGS) have expanded respiratory pathogen surveillance from targeted detection toward higher-resolution genomic characterization. However, no single technology can adequately address all surveillance objectives. Instead, molecular diagnostics, genomic sequencing, epidemiological investigation, and clinical assessment provide complementary information that can be integrated according to the pathogen, clinical context, and specific public health question. This review examines the evolution of respiratory pathogen surveillance and emphasizes an integrated framework in which different technologies are selected and combined to support pathogen characterization, transmission investigation, antimicrobial resistance surveillance, and preventive decision-making. We further discuss emerging applications of artificial intelligence (AI) for integrating genomic, epidemiological, and clinical data, with potential roles in genomic interpretation, early risk assessment, and precision infection control. Although AI-enhanced surveillance remains largely at the research and validation stage, advances in data integration, model development, and computational infrastructure may enable more predictive and actionable surveillance systems. Overall, the future of respiratory pathogen surveillance will depend not simply on adopting more advanced technologies, but on integrating complementary detection and analytical approaches to transform pathogen detection into actionable evidence for public health decision-making.

An updated phylogeny of bats&nbsp;is presented,&nbsp;based on new genome assemblies and&nbsp;many ancient fossils&nbsp;and including all known bat families.